Le parasite Leishmania à l’ère de la post-génomique
Bibliographic record
Abstract
Leishmania is a protozoan parasite responsible for considerable morbidity worldwide. The pathologies caused by Leishmania infections are varying with the species. The ongoing determination of the Leishmania major genome sequence represents a milestone for Leishmania research. We discuss here the use of transcriptomics and proteomics to accelerate our understanding of key processes related to Leishmania biology. These two techniques should be useful to find genes and proteins that are expressed in a stage-specific manner and examples of the use of such techniques are provided. Both approaches will complement each others. Indeed, while a number of stage-specific genes have increased stable RNA levels, an even larger subset of the Leishmania amastigote genes are regulated at the level of translation. The availability of the Leishmania genome should also permit important advances in finding species-specific genes that could explain different pathologies. Functional genomic and proteomic approaches should also be useful for understanding the mechanisms of drug resistance in the parasite. The availability of both the Leishmania genome and of its human host or of the mouse animal model will facilitate large scale studies and increase our understanding of host-pathogen interactions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".